Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3. However, two key categories of problem remain. Firstly there are the unique technical problems resulting from the approach taken in creating the CA, such as scope with retrieval agents and the often nonsensical answers of former generative agents. Secondly, humans perceive CAs as social actors, and as a result expect the CA to adhere to social convention. Failure on the part of the CA in this respect can lead to a poor interaction and even the perception of threat by the user. As such, this paper presents a survey highlighting a potential solution to both categories of problem through the introduction of cognitively inspired additions to the CA. Through computational facsimiles of semantic and episodic memory, emotion, working memory, and the ability to learn, it is possible to address both the technical and social problems encountered by CAs.

翻译：当前对话代理（CA）因大型语言模型（如GPT3）的影响，近年来在对话质量上有所提升。然而，仍存在两类关键问题。首先，CA构建方法本身带来了独特的技术难题，例如检索式代理的局限性以及早期生成式代理常产生的无意义回答。其次，人类将CA视为社会参与者，因而期望其遵循社会规范。CA在此方面的失败可能导致不良交互，甚至引发用户对其构成威胁的感知。为此，本文通过综述研究，提出引入认知启发式组件作为解决上述两类问题的潜在方案。通过计算模拟语义记忆与情景记忆、情感、工作记忆及学习能力，有望应对CA面临的技术与社会性挑战。